JP2007108814A - Variance analysis system - Google Patents

Abstract

<P>PROBLEM TO BE SOLVED: To provide a variance analysis support system which efficiently refines and analyzes a variance having a great influence on the quality of medical care from among variances, that is, differences between clinical paths, which are standard medical care plans, and actual medical care. <P>SOLUTION: The variance analysis system is provided with; a variance extraction means which extracts data which are accumulated in an electronic medical record system and generates variance-occurrence frequency data representing variance-occurrence frequencies and variance influence degree data representing degrees of seriousness of disorders which finally occur; a variance database which stores data including variance detection difficulty-or-easiness data representing the variance-occurrence frequency data, the variance influence degree data and difficulty or easiness in detecting the generated variances; a deviance computation means which computes deviance representing degrees of deviance from a standard medical care plan; and a variance importance computation means which computes variance importance data. <P>COPYRIGHT: (C)2007,JPO&INPIT

Description

  The present invention relates to hospital information system technology in the medical field, and more particularly to an analysis system for variance that is a difference between a standard medical care plan and actual medical care.

  A clinical path (also called a critical path) that represents a standard treatment plan in order to improve the quality of medical care by realizing standardization of medical care in a social situation that promotes the improvement and efficiency of medical care. Is emphasized. In order to improve the quality of medical care using the clinical path, it is said that it is necessary to improve the clinical path based on the basis based on the accumulated medical data. In particular, by collecting and analyzing the variance that is the difference between the standard treatment plan described in the clinical path and the actual treatment, it is possible to create a clinical path with low occurrence frequency and high medical effect. Conceivable. The variance analysis that has been conducted so far is based on the number of hospital stays, treatment results, costs, etc., and the clinical practice that is different from the standard practice plan, and the clinically desirable outcomes and goals that were not achieved. We have extracted and analyzed as variance. As a method for collecting and analyzing variance, a discharge variance method, a gateway variance method, a sentinel method, and an all variance method have been proposed according to the granularity of variance ("Patent Document 1"). Among them, the all variance method is a method for analyzing the difference from all the medical practices described in the clinical path in order to analyze the variance in detail, and the improvement effect of the clinical path is considered to be remarkable. ing. Conventionally, the collection and analysis of variances for all variance analysis has been difficult to implement because a large amount of labor is required. However, with the spread of electronic medical records, it is becoming possible to realize all variance analysis.

  Next, a conventional example related to a system that supports variance analysis and creation of a clinical path with low occurrence frequency and high medical effect will be described. In the method (conventional example 1) described in “Non-Patent Document 1”, first, a specific event such as surgery or movement from an ICU (Intensive Care Unit) to a general ward is set in advance. Next, the occurrence frequency of the variously sliced variances such as for each occurrence factor of the variance for the set event is displayed in a graph. The system described in “Patent Document 1” (Conventional Example 2) is a clinically desirable outcome and target outcome for each disease name and postoperative day and criteria for determining whether the outcome has been achieved. This is an electronic medical record system equipped with a file describing a certain assessment as a database. The system described in “Patent Document 2” (conventional example 3) is a system that divides the entire medical process into several steps for each purpose, and sets conditions for moving to the next step in each step. This makes it possible to perform medical process management with a clear standard for process extension and suspension.

JP 2004-86506 A

JP 2004-13219 A Variance Management System (Bing Net Press) pp.26-27, pp.41-42

  In the variance analysis in the prior art, emphasis was placed on the collection and management of variance. Therefore, a variance analysis method for creating a clinical path with a low occurrence frequency and a high medical effect has not been sufficiently considered. In addition, in order to perform all variance analysis, the number of subjects of variance analysis becomes enormous, so that even if all variances are acquired, there is a problem that the analysis requires a lot of labor.

  In Conventional Example 1, it was not only the grasp of the occurrence state of the variance, but in order to plan a countermeasure for the produced variance, further detailed examination was required for each individual case. Moreover, attention is paid to the frequency of occurrence in grasping the status of variance. Therefore, another approach, such as comparing the impact on treatment outcomes and length of hospital stay, was needed to find a significant variance that was less frequent but could lead to death. Conventional example 2 focuses on variance collection for quantitative variance analysis, but does not mention a method for analyzing the collected variance. Also, it does not take into account that the number of objects of variance analysis such as all variance analysis becomes enormous. Conventional Example 3 prescribes a medical practice for a presumed variance, and it is necessary to create a rule in advance which medical practice is to be performed under what conditions. However, an effective variance analysis method for creating this rule was not considered.

  As described above, in order to effectively analyze a large amount of collected variance, it is difficult to obtain a sufficient effect in the above-described conventional example.

  Accordingly, an object of the present invention is to provide a variance analysis system that assists in efficiently narrowing down the variance that significantly affects the improvement of the quality of medical care and extracting the medical practice that causes the occurrence.

  In order to achieve the above object, the variance analysis system of the present invention is an implementation record data including electronic medical record input / output means, electronic medical chart control means, and actual data of variance that is a difference between a standard medical care plan and actual medical care. In conjunction with an electronic medical record system comprising an implementation record database in which is stored and a clinical path master in which a standard medical plan is stored, and implementation record data input from the electronic medical record input / output means and the clinical path master The data on the frequency of occurrence and the data on the frequency of the occurrence of variance and the data on the degree of the effect of the variance indicating the severity of the injury caused by the occurrence of the variance are shown. The variance extraction means to be generated, the variance occurrence frequency data, and the variance From the variance database that stores the data including the variance impact data and the variance detection difficulty data that represents the difficulty of detecting the generated variance, and the data stored in the execution record data and the clinical path master, the standard A variance degree calculation means for calculating a deviation degree obtained by quantifying a deviation from a typical medical plan, and a variance importance degree data obtained by quantifying the importance degree of the variance from the data stored in the variance database and the deviation degree. A variance importance level calculating means, a screen configuration means for configuring a screen for displaying the variance importance level data, and a variance importance level presentation means for presenting a screen configured by the screen configuration means. And

Further, in the variance analysis system described above, the variance influence data generated by the variance extraction unit is generated from data including a medical accounting record describing a balance status.
Further, in the variance analysis system described above, the variance importance degree is calculated from the divergence degree, the variance occurrence frequency data, the variance influence degree data, the variance detection difficulty data, or the variance importance degree data. It has an arrangement control means for generating an arrangement list in which events to be presented by the presentation means are arranged, and the screen configuration means configures a screen based on the alignment list.

  In addition, an electronic medical record input / output means, an electronic medical chart control means, an implementation record database in which implementation record data including the actual data of variance, which is the difference between the standard medical treatment plan and the actual medical treatment, is stored, and the standard medical treatment plan The clinical path master is linked to all or one of the electronic medical record system including the clinical path master in which the medical record is stored and the medical accounting system in which the balance is recorded. A system linkage unit that acquires accumulated data and the execution record data and / or data including the medical accounting record, data accumulated in the clinical path master, and the execution record data, and Or a graph display function for displaying statistical data of a plurality of patients from the data including the medical accounting records. The graph display function takes the elapsed time consisting of days, hours, or time zones on the horizontal axis, the stage or balance indicating the patient state on the vertical axis, and points corresponding to the elapsed time for a plurality of patients as line segments. The multitrack chart that changes the drawing pattern of the line segment according to the number of patients is displayed, the occurrence frequency of the variance is displayed corresponding to the vertical axis of the multitracking chart, and the horizontal axis of the multitracking chart is displayed. It is characterized by displaying the hospital stay distribution.

  Furthermore, in the variance analysis system described above, when a line segment presented on the multi-tracking chart is selected, the variance importance level data regarding the patient group corresponding to the selected line segment is displayed on the variance importance level presentation unit. It is characterized by presenting the screen.

  Further, in the variance analysis system, when a line segment presented on the multi-tracking chart is selected, there is a medical practice visualization means for presenting a medical practice regarding a patient group corresponding to the line segment, and the medical practice visualization means includes: It consists of a medical process matrix display unit and a patient-specific frequency distribution display unit. The medical process matrix display unit displays the number of days since admission on the horizontal axis and the matrix of the medical practice and stage on the vertical axis. The color tone is changed according to the number of medical treatments and the number of occurrences of variance, and the frequency distribution display section for each patient presents a frequency distribution for all or part of the number of treatments for each patient, the number of occurrences of variance, and the cost. Set the region of interest on the graph shown in the medical process matrix display section or the patient-specific frequency distribution display section. When, characterized in that only the data that has been sliced in the region of interest corresponding on the other graph is displayed is aggregated.

  According to the present invention, it is possible to provide a variance analysis system that assists in efficiently narrowing the variance that significantly affects the improvement of the quality of medical care and extracting the medical practice that causes the occurrence.

  Hereinafter, embodiments of the present invention will be described in detail with reference to the drawings.

  FIG. 1 is a configuration diagram of a variance analysis system according to the present invention. The variance analysis system shown in FIG. 1 includes an electronic medical record input / output unit 100, an electronic medical record control unit 101, an implementation record database 102, a clinical path master 103, a variance extraction unit 104, a variance database 105, and a divergence calculation. It comprises means 106, variance importance calculation means 107, screen composition means 108, and variance importance degree presentation means 109.

  An algorithm for presenting the importance of variance using the system of this configuration will be described. This algorithm consists of variance extraction, divergence calculation, and variance importance calculation. First, the processing performed by the variance extracting means will be described. FIG. 2 shows a flowchart of processing executed by the variance extracting means. First, the clinical path master in the clinical path master 103 and the execution record data in the execution record database 102 are acquired via variance extraction means (S201). FIG. 3 shows two tables representing the clinical path master. In the action master table, the medical practice to be performed and the medical practice code are recorded. In addition, the number of days that the medical practice should be performed is recorded in the field called stage. This example shows that three medical treatments of orientation, vital check, and regular medicine check are performed at stage 0. The variance content master table describes the content of variance and the variance code that may occur in each medical practice. FIG. 4 shows an implementation record table representing implementation record data. In the implementation record table, data relating to the date when the medical practice was performed for each patient and the variance that occurred at that time are recorded. Next, the frequency is calculated for each variance code from the implementation record table, and the variance frequency data is calculated and stored in the variance database 105 (S202). Next, a variance influence degree is created from the implementation record table and stored in the variance database 105 (S203). FIG. 5 shows an example of a table indicating variance accumulated data. Here, the variance influence degree is calculated with reference to the length of stay and treatment results. In order to calculate, the average value obtained by normalizing the hospital stay and treatment results may be used as the variance influence degree, or may be calculated by adding a weight to an index to be emphasized. Next, the user creates a variance detection difficulty for each variance and stores it in the variance accumulation database (S204). S204 is not necessarily an essential step.

  Next, the divergence degree calculation processing in the divergence degree calculating means 106 will be described. The degree of divergence is a quantification of the divergence from the standard medical care plan caused by the occurrence of variance. This degree of divergence is set for each content of variance. FIG. 6 shows a flowchart for calculating the degree of deviation. First, the clinical path master in the clinical path master 103 and the execution record data in the execution record database 102 are acquired via variance extraction means (S601). Next, assuming that the number of days that patient j has achieved stage i is d (i, j), d (-1, j) = 0 is set for all patients (S602). Next, the following processing is performed for all stages i (S604-S609). First, from the clinical path master, a set SVi of variance codes of variances occurring in stage i is generated. For example, from the table shown in FIG. 3, SV0 = {V1, V2, V3, V4}. Next, assuming that the deviation degree of variance included in SVi is D (SVi), D (SVi) = 0 is set as an initial value (S606). Next, the following processing is performed for all patients j (S608-S610). First, d (i, j) is calculated from the execution record data and the action master (S608). Specifically, the medical practice to be performed at stage i is extracted from the clinical path master's action master table, and the number of days that have passed the extracted medical practice is calculated from the implementation record table. For example, from the tables shown in FIGS. 3 and 4, d (0,0) = 2. Next, {d (i, j) -d (i-1, j) -1} is added to the variance deviation degree D (SVi), and j is incremented by 1 (S609). In the tables shown in FIG. 3 and FIG. 4, when i = 0 and j = 0, as a result of processing S609, the variance degree D (SVi) = 1. The value calculated here reflects that it was performed on the next day because the regular medicine check to be performed at stage 0 was forgotten.

  Finally, the variance importance calculation processing will be described. In this process, the variance importance calculation means 107 calculates the variance importance from the variance frequency data, the variance influence data, and the variance detection difficulty data. In order to calculate the importance of variance, the occurrence frequency of variance, the influence of variance, the difficulty of detecting variance, and the divergence are multiplied. An example of variance importance data is shown in FIG. However, although the importance of variance is calculated here in consideration of detection difficulty, it is not always necessary. When the detection difficulty is not taken into consideration, the importance of the variance may be obtained by multiplying the influence degree of the variance, the occurrence frequency of the variance, and the divergence degree.

The variance importance calculated in this way constitutes a screen for displaying the variance importance data by the screen construction means 108, and the screen is used to display the screen in the variance importance presentation means 109 and the medical action or variance that the user wants to refer to. Displays the importance of variance for.
Here, a screen presented by the variance importance degree presentation unit 109 will be described. FIG. 8 is a diagram showing an example of a screen presented by the variance importance degree presenting means 109 of the present invention. First, a focused clinical path name is selected on the variance selection means display unit 801 on the left side of the figure. When paying attention to all clinical paths, the all selection button 8011 is pressed. On the other hand, when canceling the selection in all clinical paths, the all cancel button 8012 is pressed. An index related to the importance of variance generated in the clinical path selected in this way is presented to the right-side variance importance presentation unit 802 in the figure. In FIG. 8, four indicators, namely, the variance influence degree, the variance frequency, the variance detection difficulty, and the divergence degree are displayed in a radar chart format. Since the area of the quadrangle formed by each line segment corresponds to the magnitude of the importance of variance, it is possible to identify a clinical path in which a variance that requires countermeasures immediately occurs. Further, when displaying various indexes, it is possible to intuitively grasp the value of each index constituting the importance by using the radar chart format as shown in FIG. In the medical field, it may not be desirable to make a judgment based solely on the importance of variance, such as in cases where fatalities occur even if the frequency is low, and it is useful for grasping the value of each indicator. . In FIG. 8, the clinical path “(1) radical prostatectomy” and “(5) hepatocellular carcinoma (TAE)” is selected in the variance selection means display unit 801. As a result, the variance importance degree presentation unit 802 presents each variance influence degree, variance frequency, variance detection difficulty, and divergence degree. The user may take measures for “(5) hepatocellular carcinoma (TAE)” with a large square area and a high value of variance importance, or “(1) prostate with low frequency but high divergence value” You may also take measures for "total excision".

  Here, when it is determined that analysis of the variance of “(5) hepatocellular carcinoma (TAE)” is necessary, the screen transitions to FIG. Analysis can be performed. In FIG. 9, by selecting a check box for an outcome in hepatocellular carcinoma (TAE), it is possible to select an outcome of interest. The figure shows a state in which outcomes “(1) Ready” and “(5) Pain / fever can be controlled outpatiently” are selected. As a result, the variance importance degree presentation unit 802 presents the variance influence degree, variance frequency, variance detection difficulty, and divergence degree. As in FIG. 8, the user may take measures for “pain / fever can be controlled in the outpatient” with a large square area and a high value of variance importance, or less frequently but with a high value of divergence. You may also take measures for "Ready". In this way, in order to analyze a large amount of variance, it is possible to narrow down important variances using not only the frequency but also an index such as the degree of deviation.

  In FIGS. 8 and 9, attention is paid to the importance of variance. However, when it is desired to refer to various data relating to variance such as variance distribution of variance and change in the number of occurrences of variance, the detailed statistics shift button 8013 is pressed and the variance shown in FIG. Move to the detailed statistics screen. In the variance detail statistics screen shown in FIG. 10, the factor-based early variance screening presentation unit 1001 at the top of the screen presents a variance that matches a preset extraction condition. The extraction conditions are, for example, a variance generation period or a variance generation factor. In order to reset the extraction condition, the extraction condition setting button 10011 may be pressed and reset. Further, the accumulated variance is analyzed at various points of cut by the path screening multidimensional data analysis presentation unit 1002 at the bottom of the screen. FIG. 10 shows the number of days of hospitalization by path, the number of occurrences of variance by path, the number of patients with occurrence of variance by path, and the number of occurrence of variance by path. As a method for realizing the multidimensional data analysis and presentation unit 1002 for path screening, it is conceivable to use a system called OLAP (On-Line Analytical Processing) for analyzing and visualizing accumulated data in a multidimensional manner. By utilizing such a screening screen, it is possible to analyze the status of variance from the viewpoint desired by the user.

  Here, the hardware configuration and operation of the variance analysis system described so far will be described. FIG. 11 shows a hardware configuration diagram for realizing the variance analysis system. The electronic medical record input / output means 100 corresponds to the keyboard 11010 and the display 11011. The electronic medical chart control means 101 is stored as a program code on the disk 11014 and is expanded in the memory 11012 and the central processing unit 11013 when used. Similarly, the variance extracting unit 104, the divergence calculating unit 106, the variance importance calculating unit 107, and the screen configuration unit 108 are stored as program codes on the disk 11024, respectively, and are used in the memory 11022 and the central processing unit 11023 when used. Be expanded. Data stored in the variance database 105 is stored in the disk 11024. The variance detection difficulty data is stored in the disk 11024 via the display 11021 and the keyboard 11020. Furthermore, the screen configured by the screen configuration unit 108 is displayed on the display 11021 corresponding to the variance importance degree presentation unit 109.

  FIG. 12 is a configuration diagram of a variance analysis system according to the present invention. In particular, in order to effectively narrow down the medical practice presented by the variance importance degree presenting means, it is characterized in that the clinical practice presented on the variance selection means display unit 801 and the outcome are aligned. The variance analysis system shown in FIG. 12 is obtained by adding an alignment control means 110 to the configuration diagram shown in FIG. Here, a mechanism for aligning the medical practice and outcome presented on the variance selection means display unit 801 using the alignment control means 110 will be described. FIG. 13 is an example of a screen presented by the variance importance degree presentation unit 109. FIG. 14 is a sequence diagram related to the alignment process. When aligning, first, an alignment criterion is selected using a radio button in the alignment display unit 803 shown in FIG. 13, and an alignment setting button 8031 is pressed. Next, the selected alignment reference is transmitted to the alignment control unit 110 via the screen configuration unit 108. The alignment control unit 110 acquires the index selected as the transmitted alignment criterion, generates a list of medical practices and outcomes that are aligned based on the selected index, and transmits the list to the screen configuration unit 108. The screen composition unit 108 composes a screen as shown in FIG. 13 based on the transmitted list and presents it to the variance importance degree presentation unit 109. As described above, by providing the alignment control unit 110, even if a large amount of medical practice or outcome is presented on the variance selection unit display unit 801, it is possible to refer to an index from an item that the user is highly interested in. It is possible to effectively narrow down important variances.

  This section describes how to calculate the variance impact data taking into account medical accounting data. FIG. 15 is a configuration diagram of a variance analysis system according to the present invention. In particular, the part for calculating the variance influence data is extracted. The variance analysis system shown in FIG. 15 includes an electronic medical record input / output unit 100, an electronic medical record control unit 101, an implementation record database 102, a clinical path master 103, a variance extraction unit 104, a variance database 105, and a medical accounting database. 111, medical accounting control means 112, and medical accounting input / output means 113. FIG. 16 shows an example of a table indicating variance influence data. In this example, an average of values obtained by normalizing hospital stay days, costs, and treatment results is expressed as a variance influence degree for each variance code. In addition, the value of the variance influence degree is created in advance by the Clinical Pass Committee etc. from the viewpoint of hospital stay, treatment results, and balance, and the user calculates the variance influence degree while referring to the created standard. Also good. The Clinical Path Committee is a committee related to the introduction and management of clinical paths, and is composed of nurses, doctors, clerical staff, engineers, medical record managers, and the like. The reason why the user calculates in this way is that, at the initial stage of creating the clinical path, many variances occur and it is necessary to improve the clinical path through trial and error.

  When calculating the importance of variance using various indicators in this way, for example, by adding a function to see the detailed values of each indicator, for example, the length of stay, cost, and treatment for determining the impact value It is possible to grasp a fine-grained index such as a grade. Specifically, it is sufficient to provide a mechanism that presents a finer granularity index such as the length of hospital stay or cost by pressing near the influence level. FIG. 17 shows an example of a screen that displays an index in which the variance influence degree is developed.

  Thus, by calculating the variance influence degree data in consideration of medical accounting data, it is possible to perform an effective variance analysis from the viewpoint of the medical economy.

  FIG. 18 is a configuration diagram of a variance analysis system according to the present invention. In particular, it is characterized by a portion that visualizes patient groups that deviate from the standard plan. The variance analysis system shown in FIG. 18 is an extension of the configuration diagram shown in FIG. The newly added components in the configuration diagram of the variance analysis system shown in FIG. 18 are a system linkage unit 114 and a graph generation unit 115. The system linkage unit 114 acquires data including a clinical path master and implementation record data and / or medical accounting records. The graph shown in FIG. 19 is generated from the acquired data. Here, the graph shown in FIG. 19 will be described. In the multi-tracking chart display unit 1901, the horizontal axis indicates the number of elapsed days and the vertical axis indicates the patient state, and the progress of the medical practice of a plurality of patients is displayed. Each broken line is a line segment connecting points representing the stage of the patient state at the time of each elapsed day, and the line segment drawing pattern is changed according to the number of patients. In FIG. 19, the drawing pattern is changed using the type of line segment in order to express the number of patients, but the number of patients may be expressed by the thickness of the line segment. Further, the vertical axis may take a balance instead of the stage. Furthermore, the horizontal axis may take elapsed time such as time or time zone instead of elapsed days.

  Next, data necessary for realizing the graph shown in the multi-tracking chart display unit 1901 will be described. The data structure is shown in FIG. In order to realize this graph, first, the number of days that have passed the stage is acquired from the implementation information database 102 and the clinical path master 103. The obtained information is patterned and the frequency is calculated for each pattern.

  Here, how to read the line segment of the multi-tracking chart display unit 1901 will be described. It is assumed that the stage of the patient state on the vertical axis is set based on the medical goal to be achieved every day. In this case, when medical treatment is performed according to the set clinical path, it is represented by a straight line having an inclination of 1 and an intercept of 0. In this graph, when the medical care is completed earlier than a set clinical path called positive variance, a fold line is located on the upper left side of a straight line having an inclination of 1 and an intercept of 0. On the other hand, when the medical care is completed later than a set clinical path called negative variance, a fold line is located on the lower right side of a straight line having an inclination of 1 and an intercept of 0.

  In the variance frequency distribution display unit 1902, the variance frequency distribution in each stage is represented by a bar graph. Here, a stacked graph according to variance factors may be used. Here, both the stage data representing the patient state and the data relating to the variance factor are characterized by summing up using the stage as a key. This makes it possible to observe two numerical values of stage delay and variance factors simultaneously. The hospital days frequency distribution display unit 1903 represents the frequency distribution of hospital days as a bar graph. By using such a graph, it is possible not only to understand the trend of hospital days, but also to understand which stage has the most variance and what caused it. By identifying a stage with a large variance, it is possible to grasp how the variance affects the length of stay and the income and expenditure.

  FIG. 21 shows an efficient analysis of a large amount of variance by using the graph shown in Example 4 as a selection means in order to quickly find a medical practice pattern and an important variance as a bottleneck. 3 is a flowchart relating to a method. FIG. 22 is obtained by expanding the screen shown in FIG. 19 and adding selection means. First, in the multi-tracking display unit 1901, a line segment indicating a patient group of interest is selected (S2101). At the same time, the analysis cooperation selection unit 1904 selects a system for performing detailed analysis. Next, when “(1) Variance importance” is selected in the analysis cooperation selection unit 1904 (S2102), by pressing a transition button 19041 in the analysis cooperation selection unit 1904, the first embodiment or the example 2 and the screen used in Example 3 is displayed, and the importance of variance related to the selected patient group is displayed (S2103). In addition, when “(2) Clinical process visualization” is selected in the analysis cooperation selection unit 1904 (S2104), by pressing a transition button 19041 in the analysis cooperation selection unit 1904, the details of the selected patient group are displayed. The screen transitions to a screen that visualizes the medical care process (S2105).

  FIG. 23 shows a screen that visualizes the medical process transitioned in S2005. Here, the screen shown in FIG. 23 will be described. FIG. 23 includes a medical process matrix display unit 2301 and a frequency distribution display unit 2302 for each patient. The medical process matrix display unit 2301 takes the number of days since hospitalization on the horizontal axis and the medical practice and stage on the vertical axis, and the color tone of each cell is changed according to the number of clinical actions and the number of occurrences of variance. The patient-specific frequency distribution display unit 2302 represents a frequency distribution such as the number of medical treatments for each patient, the number of occurrences of variance, and the cost. In FIG. 23, when a region of interest is set on both graphs shown in the medical care process matrix display unit 2301 and the patient-specific frequency distribution display unit 2302, only the data sliced in the corresponding region of interest on the other graph is aggregated. It has a function to be displayed. Thereby, it becomes easy to grasp the occurrence of variance and the status of medical practice for each patient group.

  In this way, by providing a selection unit that focuses on a patient group that deviates from the standard plan, it is possible to identify a variance or medical practice to be focused on.

It is a typical block diagram of the variance analysis assistance system in this invention. It is a flowchart for extracting a variance by the variance extraction means in this invention. It is a figure showing a clinical path master in the present invention. It is a figure showing the implementation record table in this invention. It is a figure showing a variance influence degree table in this invention. It is a flowchart for calculating the deviation degree in the present invention. It is a figure showing a variance importance degree table in this invention. It is a 1st figure which shows the screen which presents the data relevant to the variance importance in the variance analysis assistance system in this invention. It is a 2nd figure which shows the screen which presents the data relevant to the variance importance in the variance analysis assistance system in this invention. It is a figure which shows an example of the screen relevant to the detailed statistics regarding variance in the variance analysis support system in this invention. It is a block diagram of the hardware of the variance analysis assistance system in this invention. It is a 2nd typical block diagram of the variance analysis assistance system in this invention. It is a 3rd figure which shows the screen which shows the data relevant to the variance importance in the variance analysis assistance system in this invention. It is a sequence diagram which comprises the screen which shows a variance importance in this invention. It is a typical block diagram in the function which calculates the variance influence degree of the variance analysis support system in this invention. It is a 2nd figure showing a variance influence degree table in this invention. It is a 4th figure which shows the screen which shows the data relevant to the variance importance in the variance analysis assistance system in this invention. It is a typical block diagram in the function which visualizes a mode that deviated from the standard plan of the variance analysis support system in this invention. It is a 1st figure which shows the graph which visualized a mode that deviated from the standard plan of the variance analysis assistance system in this invention. It is a figure showing the data for producing | generating the graph which visualized a mode that deviated from the standard plan in this invention. It is a flowchart regarding the method of efficiently analyzing a large amount of variance in the present invention. It is a 2nd figure which shows the graph which visualized a mode that deviated from the standard plan of the variance analysis assistance system in this invention. It is a figure which shows an example of the screen which visualized the medical treatment process in the variance analysis assistance system in this invention.

Explanation of symbols

DESCRIPTION OF SYMBOLS 100 ... Electronic medical record input / output means, 101 ... Electronic medical chart control means, 102 ... Execution information database, 103 ... Clinical path master, 104 ... Variance extraction means, 105 ... Variance database, 106 ... Deviation degree calculation means, 107 ... Variance importance Calculation means 108 ... Screen configuration means 109 109 Variance importance presentation means 110 ... Alignment control means 111 ... Medical accounting database 112 ... Medical accounting control means 113 113 Medical accounting input / output means 114 ... System cooperation means DESCRIPTION OF SYMBOLS 115 ... Graph production | generation means, 801 ... Variance selection means display part, 8011 ... All selection button, 8012 ... All release button, 8013 ... Detailed statistics transfer button, 8014 ... Drill down button, 802 ... Variance importance presentation part, 803 ... Arrangement Display unit, 8031 ... Alignment setting button DESCRIPTION OF SYMBOLS 1001 ... Early variation screening presentation part according to factor, 10011 ... Extraction condition setting button, 1002 ... Multidimensional data analysis presentation part for path screening, 11010 ... Keyboard, 11011 ... Display, 11012 ... Memory, 11013 ... Central processing unit, 11014 ... Disk , 11020 ... keyboard, 11021 ... display, 11022 ... memory, 11023 ... central processing unit, 11024 ... disk, 1901 ... multi-tracking chart display unit, 1902 ... variance frequency distribution display unit, 1903 ... hospital frequency distribution display unit, 1904 ... Analysis cooperation selection part, 19041 ... Transition button, 2301 ... Medical process matrix display part, 2302 ... Patient frequency distribution display part.

Claims (6)

Electronic medical record input / output means, electronic medical chart control means, and a clinical record database and standard medical care plan in which clinical record data including performance data of variance, which is the difference between standard medical care plan and actual medical care, is stored In conjunction with an electronic medical record system comprising a clinical path master, the implementation record database input from the electronic medical record input / output means and the data stored in the clinical path master are extracted to determine the frequency of occurrence of variance. Variance extraction means for generating the variance occurrence frequency data indicating the variance occurrence frequency data indicating the degree of severity of injury finally caused by occurrence of variance and the occurrence frequency of the variance occurrence,
A variance database storing data including the variance occurrence frequency data, the variance influence data, and / or the variance detection difficulty data representing the detection difficulty of the generated variance.
A deviation degree calculating means for calculating a deviation degree obtained by quantifying a deviation from a standard medical care plan from the execution record data and the data accumulated in the clinical path master,
Variance importance calculating means for calculating variance importance data obtained by quantifying the importance of variance from the data stored in the variance database and the divergence;
Screen configuration means for configuring a screen for displaying the variance importance data;
And a variance importance degree presenting means for presenting a screen constituted by the screen constituting means. The variance analysis system according to claim 1,
The variance influence degree data generated by the variance extractor is generated from data including medical accounting records describing the balance status. 2. The variance analysis system according to claim 1, wherein the variance is calculated from the degree of deviation, the variance occurrence frequency data, the variance influence data, the variance detection difficulty data, or the variance importance data. An alignment control means for generating an alignment list in which events to be presented by the importance degree presentation means are arranged;
The screen composition means configures a screen based on the sorted list, a variance analysis system characterized in that Electronic medical record input / output means, electronic medical chart control means, and a clinical record database and standard medical care plan in which clinical record data including performance data of variance, which is the difference between standard medical care plan and actual medical care, is stored Stored in the clinical path master in cooperation with all or one of the electronic medical record system including the clinical path master and the medical accounting system in which the medical accounting records describing the balance status are stored. And a system linkage unit that obtains data including the implementation record data and / or the medical accounting record,
A graph display function for displaying statistical data of a plurality of patients from the data accumulated in the clinical path master and the execution record data and / or the data including the medical accounting record;
The graph display function takes the elapsed time consisting of days, hours, or time zones on the horizontal axis, the stage or balance indicating the patient state on the vertical axis, and points corresponding to the elapsed time for a plurality of patients by line segments. A multi-tracking chart that changes the line drawing pattern according to the number of patients is displayed, the frequency of occurrence of variance is displayed corresponding to the vertical axis of the multi-tracking chart, and the horizontal axis of the multi-tracking chart is displayed. A variance analysis system characterized by displaying the hospital day distribution.   5. The variance analysis system according to claim 1, wherein when a line segment presented on the multi-tracking chart is selected, the variance importance degree data relating to a patient group corresponding to the selected line segment is input to the variance importance degree presentation unit. A variance analysis system characterized by presenting a screen for display. The variance analysis system according to claim 4, further comprising: a medical practice visualization means for presenting a medical practice related to a patient group corresponding to a line segment when the line segment presented on the multi-tracking chart is selected.
The medical practice visualization means is composed of a medical process matrix display unit and a frequency distribution display unit for each patient. In the medical process matrix display unit, the horizontal axis indicates the number of days since hospitalization, and the vertical axis indicates the medical practice and stage. A matrix is displayed, and the color tone of each cell is changed in accordance with the number of medical treatments and the number of occurrences of variance. In the frequency distribution display section for each patient, the number of treatments for each patient, the number of occurrences of variance, and the cost are all or When the region of interest is set on the graph shown in the clinical process matrix display unit or the patient-specific frequency distribution display unit, the frequency distribution of a part is presented, and the data sliced in the corresponding region of interest on the other graph Variance analysis system characterized by only being aggregated and displayed.

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